State Key Laboratory of Crop Stress Adaptation and Improvement, Henan Joint International Laboratory for Crop Multi-Omics Research, School of Life Sciences, Henan University, Kaifeng, 475004, China.
Thermo Fisher Scientific, Shanghai, 201206, China.
J Integr Plant Biol. 2024 Nov;66(11):2329-2345. doi: 10.1111/jipb.13774. Epub 2024 Sep 10.
The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.
利用代谢组学方法来探索植物适应动态环境的适应性和适应性的代谢机制正在增长，这凸显了需要一个高效且用户友好的工具包，专门用于分析代谢组学研究产生的广泛数据集。目前的代谢组数据分析协议通常难以处理大规模数据集，或者需要编程技能。为了解决这个问题，我们提出了 MetMiner（https://github.com/ShawnWx2019/MetMiner），这是一个用户友好的、全功能的管道，专门用于植物代谢组学数据分析。MetMiner 构建在 R shiny 上，可以部署在服务器上，以利用额外的计算资源来处理大规模数据集。MetMiner 确保在整个分析过程中的透明度、可追溯性和可重复性。其直观的界面提供了强大的数据交互和图形功能，使用户无需事先具备编程技能即可深入参与数据分析。此外，我们构建并集成了一个特定于植物的质谱数据库到 MetMiner 管道中，以优化代谢物注释。我们还开发了 MDAtoolkits，其中包括一套用于统计分析、代谢物分类和富集分析的完整工具，以促进从数据集中挖掘生物学意义。此外，我们提出了一种迭代加权基因共表达网络分析策略，用于在大规模代谢组学数据挖掘中进行高效的生物标志物代谢物筛选。在两个案例研究中，我们验证了 MetMiner 在数据挖掘中的效率和在代谢物注释中的稳健性。总之，MetMiner 管道代表了一种有前途的植物代谢组学分析解决方案，为科学界提供了一个易于使用的有价值的工具。
